CE2STS-Statistical analysis 1

Module Provider: School of Construction Management and Engineering, School of Built Environment
Number of credits: 10 [5 ECTS credits]
Level:5
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2022/3

Module Convenor: Dr Eugene Mohareb
Email: e.mohareb@reading.ac.uk

Type of module:

Summary module description:

This module introduces key statistical methods to assess real-world engineering problems. It explains how to use basic statistical tools and introduces quantitative data analysis methods that are useful in engineering subjects including Architectural Engineering. Using a number of datasets from a range of science and engineering applications, students will learn practical statistical techniques and fundamental principles, as well as usingÌýsoftware of their choiceÌýto analyse data. This module leads on to the study of more advanced statistical techniques including probability analysis in the module of Statistical Analysis 2 (CE3STS).

Aims:

The aim of this module is to provide students with principles of statistical data analysis requiredÌýfor the evaluation of a dataset and drawing an informed and unbiased statistical conclusion.

Assessable learning outcomes:

On successful completion of this module the student should be able to:

• Explain assumptions underlying statistical techniques,


• Present and interpret statistical results scientifically,


• Select and apply statistical techniques for exploring data,


• Apply statistical methods using statistical software,


• Determine the effect of samples and populations on the results of statistical analysis,


• Perform data fitting and to evaluate the fit results, including errors and goodness-of-fit.

Additional outcomes:

• To explain how errors may be propagated in the process of statistical analysis,


• To apply linear regression on a set of data,


• To explain the limitations of regression analysis.

Outline content:

• The basic concept of measurement


• Samples and populations


• Inferential vs descriptive statistics


• Analysis of varianceÌý


• Linear regression analysis


• Confidence intervals


• Random vs systemic error


• Presenting and summarising data

Global context:

The skills and knowledge that students will acquire from this module have global applications.

Brief description of teaching and learning methods:

Teaching in this module will be by means of lectures, tutorials and practical classes. These sessions will be complemented by project activities and guided independent study.

Independent study hours needed depend on the learning style of each individual. The following guide for independent study hours is just an example.